Bibliographic record
Abstract
This paper outlines a preliminary study of the kinds of strategies that master students draw upon for interpreting and enacting their identities in online learning environments. Based primarily on the seminal works of Goffman (1959) and Foucault (1988), the Web of Identity Model (Koole, 2009; Koole and Parchoma, 2012) is used as an underlying theoretical framework for this research study. The WoI model suggests that there are five major categories of “dramaturgical” strategies: technical, political, structural, cultural, and personal-agential. In the data collection, five online master of education students participated in semi-structured, online interviews. Phenomenography guided the data collection and analysis resulting in an outcome space for each strategy of the WoI model. The study results indicate that online learners actively employ a variety of strategies in interpreting and enacting their identities. The outcome spaces provide insights into ways in which online learners can manage their identity performances and strategies for ontological re-alignment (reconceptualization of oneself). Further study has the potential to elucidate how learning designers and online instructors might facilitate such identity-work in order to shape productive online environments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".